Patterns of output firing generated by a many-input neuronal model for different model parameters and patterns of...

A relatively simple neuronal model with a large number of 'synaptic' inputs is described. The model has been extensively studied in order to investigate the influence which patterns of presynaptic firing may have upon the pattern of output firing. The model has been studied in two versions. In one the threshold remained constant, while in the other the threshold was increased following an output firing. This increase in threshold decayed with time and was analogous to the after-hyperpolarization of a real neurone. The effect on the frequency of output firing of changes in the threshold and of the overall input rate were studied in detail. The pattern of output firing was studied for two patterns of input firing. In one, each presynaptic input fired steadily, each at a different rate; in the other, the input firings were randomly distributed in time, being generated by a Poisson process. It was found that, for a given total input rate, the pattern of output firing was markedly more regular when the input processes were regular, even though the mean rate of output firing was not appreciably different for the two different distributions of input firings. It is shown that, with suitable parameters for the model, it is possible to mimic very closely the discharge of tonically discharging motoneurones under different experimental conditions. This suggests that the more general properties of the model may have considerable relevance to the way in which real neurones integrate their synaptic input.